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Pau Riba, Alicia Fornes, & Josep Llados. (2017). Towards the Alignment of Handwritten Music Scores. In Bart Lamiroy, & R Dueire Lins (Eds.), International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges (Vol. 9657, pp. 103–116). LNCS.
Abstract: It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
Keywords: Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment
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Debora Gil, Oriol Ramos Terrades, Elisa Minchole, Carles Sanchez, Noelia Cubero de Frutos, Marta Diez-Ferrer, et al. (2017). Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer. In 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging (Vol. 10550, pp. 151–159). LNCS.
Abstract: Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
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C. Gratin, Jordi Vitria, F. Moreso, & D. Seron. (1994). Texture Classification using Neural Networks and Local Granulometries. In EURASIP Workshop, Mathematical Morphology and Its Applications to image Processing, J.Serra and P.Soille, editors (pp. 309–316).
Keywords: Neural Networks; Granulometry; Kidney; Texture; Classication
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Dipam Goswami, Yuyang Liu, Bartlomiej Twardowski, & Joost Van de Weijer. (2023). FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning. In 37th Annual Conference on Neural Information Processing Systems.
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Kai Wang, Fei Yang, Shiqi Yang, Muhammad Atif Butt, & Joost Van de Weijer. (2023). Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing. In 37th Annual Conference on Neural Information Processing Systems.
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Diego Porres. (2021). Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks. In Machine Learning for Creativity and Design, Neurips Workshop.
Abstract: Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL.
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Fei Yang, Luis Herranz, Yongmei Cheng, & Mikhail Mozerov. (2021). Slimmable compressive autoencoders for practical neural image compression. In 34th IEEE Conference on Computer Vision and Pattern Recognition (pp. 4996–5005).
Abstract: Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders (SlimCAEs), where rate (R) and distortion (D) are jointly optimized for different capacities. Once trained, encoders and decoders can be executed at different capacities, leading to different rates and complexities. We show that a successful implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs. Our experiments show that SlimCAEs are highly flexible models that provide excellent rate-distortion performance, variable rate, and dynamic adjustment of memory, computational cost and latency, thus addressing the main requirements of practical image compression.
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Hugo Bertiche, Meysam Madadi, Emilio Tylson, & Sergio Escalera. (2021). DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation. In 19th IEEE International Conference on Computer Vision (pp. 5471–5480).
Abstract: We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.
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German Barquero, Sergio Escalera, & Cristina Palmero. (2023). BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction. In IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 2317–2327).
Abstract: Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of
the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP.
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Yuyang Liu, Yang Cong, Dipam Goswami, Xialei Liu, & Joost Van de Weijer. (2023). Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection. In 20th IEEE International Conference on Computer Vision (pp. 11367–11377).
Abstract: In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model.
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Jordy Van Landeghem, Ruben Tito, Lukasz Borchmann, Michal Pietruszka, Pawel Joziak, Rafal Powalski, et al. (2023). Document Understanding Dataset and Evaluation (DUDE). In 20th IEEE International Conference on Computer Vision (pp. 19528–19540).
Abstract: We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.
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Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, & Thomas B. (2023). Which Tokens to Use? Investigating Token Reduction in Vision Transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops.
Abstract: Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens.
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Xavier Soria, Yachuan Li, Mohammad Rouhani, & Angel Sappa. (2023). Tiny and Efficient Model for the Edge Detection Generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops.
Abstract: Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0:2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED.
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Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, & Joost Van de Weijer. (2023). Continual Evidential Deep Learning for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 3444–3454).
Abstract: Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.
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Valeriya Khan, Sebastian Cygert, Bartlomiej Twardowski, & Tomasz Trzcinski. (2023). Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 3496–3500).
Abstract: In this work, we improve the generative replay in a continual learning setting. We notice that in VAE-based generative replay, the generated features are quite far from the original ones when mapped to the latent space. Therefore, we propose modifications that allow the model to learn and generate complex data. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios.
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